{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cvxpy as cp\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "\n",
    "m = 20 # number of devices\n",
    "n = 5  # number of servers\n",
    "\n",
    "wD = np.random.randint(1 ,4 ,m )\n",
    "wS = np.random.randint(1 ,4 ,n )\n",
    "\n",
    "BD = np.random.uniform(50, 80, m)\n",
    "BS = np.random.uniform(100, 300, n)\n",
    "CD = np.random.uniform(100, 200, m)\n",
    "\n",
    "class Trace(object):\n",
    "    def __init__(self):\n",
    "        data = np.loadtxt('trace_data/Lumos5G-v1.0/Lumos5G-v1.0.csv', delimiter=',', skiprows=1, usecols=(0,15)).astype(int)\n",
    "        self.traces = []\n",
    "        for run in np.unique(data[:,0]):\n",
    "            trace = data[data[:,0]==run, 1]\n",
    "            trace = 1/(trace + 1)\n",
    "            self.traces.append(trace)\n",
    "        self.average = [np.average(trace) for trace in self.traces]\n",
    "        self.trace_selection = np.random.choice(len(self.traces), (m,n))\n",
    "        self.trace_counter = np.zeros_like(self.trace_selection)\n",
    "        for i in range(m):\n",
    "            for j in range(n):\n",
    "                self.trace_counter[i,j] = np.random.choice(len(self.traces[self.trace_selection[i,j]]))\n",
    "\n",
    "    def generate(self):\n",
    "        rt = np.zeros((m, n))\n",
    "        for i in range(m):\n",
    "            for j in range(n):\n",
    "                rt[i,j] = self.traces[self.trace_selection[i,j]][self.trace_counter[i,j]]\n",
    "                self.trace_counter[i,j] += 1\n",
    "                if self.trace_counter[i,j] == len(self.traces[self.trace_selection[i,j]]):\n",
    "                    self.trace_counter[i,j] = 0\n",
    "        return rt\n",
    "\n",
    "    def avg(self):\n",
    "        rt = np.zeros((m, n))\n",
    "        for i in range(m):\n",
    "            for j in range(n):\n",
    "                rt[i,j] = self.average[self.trace_selection[i,j]]\n",
    "        return rt\n",
    "\n",
    "# f(x, y, mu)\n",
    "def f(x, y, mu, gamma=0):\n",
    "    a = np.multiply(x[0, :], y)\n",
    "    b = x[1:, :] @ y\n",
    "    c = mu @ b\n",
    "    d = np.sum( 1 /np.sqrt(a + wD)) \\\n",
    "           + np.sum( 1 /np.sqrt(np.sqrt(b+ wS)))\n",
    "    e = np.sum(c)/m/n\n",
    "    # print('x0y:\\t%.2f +- %.2f' %(np.average(a), np.std(a)))\n",
    "    # print('xy:\\t%.2f +- %.2f' %(np.average(b), np.std(b)))\n",
    "    # print('uxy:\\t%.2f +- %.2f' %(np.average(c), np.std(c)))\n",
    "    # print('d:\\t%.2f' %(d))\n",
    "    # print('e:\\t%.2f' %(e))\n",
    "    return d + gamma * e, np.average(a), np.average(b), np.average(c), np.average(d), gamma * np.average(e)\n",
    "\n",
    "\n",
    "# min f(x, y. mu)\n",
    "def oracle(y, mu, gamma=0, hard=False):\n",
    "    if hard:\n",
    "        gamma = 0\n",
    "\n",
    "    Y = cp.Parameter((m,), nonneg=True)\n",
    "    Y.value = y\n",
    "\n",
    "    x = cp.Variable((n + 1, m), nonneg=True)\n",
    "\n",
    "    obj = cp.sum(cp.inv_pos(cp.sqrt(cp.multiply(x[0, :], y) + wD))) \\\n",
    "          + cp.sum(cp.inv_pos(cp.sqrt(x[1:, :] @ y + wS))) + gamma * cp.sum(mu @ (x[1:, :] @ y))/m/n\n",
    "\n",
    "    constraints = [0 <= x, x <= 1,\n",
    "                   cp.multiply(x[0, :], y) <= BD,\n",
    "                   x[1:, :] @ y <= BS,\n",
    "                   cp.sum(x, 0) == 1]\n",
    "\n",
    "    if hard:\n",
    "        constraints.append(mu @ (x[1:, :] @ y) <= CD)\n",
    "\n",
    "    prob = cp.Problem(cp.Minimize(obj), constraints)\n",
    "    prob.solve()  # Returns the optimal value.\n",
    "    return x.value, prob.value, prob.status\n",
    "\n",
    "\n",
    "trace_gen = Trace()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.38781698]\n",
      "[67.00901535]\n",
      "[130.13353859]\n",
      "[272.46963203]\n",
      "[3.93842434]\n",
      "[5.44939264]\n"
     ]
    }
   ],
   "source": [
    "N = 3 # number of experiments\n",
    "T = 500 # rounds per experiment\n",
    "reg = np.zeros((N,T))\n",
    "gamma = [0.1, 1, 10]\n",
    "\n",
    "#statistics\n",
    "stats = np.zeros((N, T, 6))\n",
    "\n",
    "for u in range(N):\n",
    "    mu = np.random.rand(m, n)\n",
    "    mu_hat = np.zeros_like(mu) # empirical mean\n",
    "    T_ij = np.ones_like(mu) # total number of times arm (i,j) is played\n",
    "    for t in range(T):\n",
    "        y = np.random.uniform(80, 120, m).astype(int)\n",
    "        x_opt, f_opt, status = oracle(y, mu, gamma)\n",
    "\n",
    "        fv, *stat = f(x_opt, y, mu, gamma)\n",
    "        stats[u, t] = np.array([fv, *stat])\n",
    "\n",
    "        if 'optimal' not in status:\n",
    "            print('Solution infeasible')\n",
    "            break\n",
    "\n",
    "        rho_ij = np.sqrt(3*np.log(t+1)/(2*T_ij))\n",
    "        mu_bar = np.max(mu_hat - rho_ij, 0) # LCB\n",
    "        \n",
    "        x_t, f_t, status = oracle(y, mu_bar, gamma)\n",
    "        f_t, *_ = f(x_t, y, mu, gamma)\n",
    "        \n",
    "        # sample j based on x_t[i], observe c_ij, update mu_hat[i,j]        \n",
    "        for i in range(m):\n",
    "            j = np.random.choice(n+1, p=x_t[:,i])\n",
    "            if j != 0:\n",
    "                j -= 1\n",
    "                c_ij = int(np.random.rand() < mu[i,j])\n",
    "                T_ij[i, j] += 1\n",
    "                mu_hat[i, j] += (c_ij - mu_hat[i, j]) / T_ij[i, j]\n",
    "\n",
    "        # calculate regert\n",
    "        reg[u, t] = f_t - f_opt      \n",
    "        \n",
    "plt.plot(np.cumsum(reg,axis=1).T)\n",
    "plt.show()\n",
    "print(np.average(stats[:,:,0], 1))\n",
    "print(np.average(stats[:,:,1], 1))\n",
    "print(np.average(stats[:,:,2], 1))\n",
    "print(np.average(stats[:,:,3], 1))\n",
    "print(np.average(stats[:,:,4], 1))\n",
    "print(np.average(stats[:,:,5], 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "/Users/yihu/opt/anaconda3/envs/Offloading-FL/lib/python3.9/site-packages/cvxpy/problems/problem.py:1278: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n",
      "  warnings.warn(\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 3 # number of experiments\n",
    "T = 500 # rounds per experiment\n",
    "reg = np.zeros((N,T))\n",
    "gamma = 1\n",
    "for u in range(N):\n",
    "    mu = trace_gen.avg()\n",
    "    mu_hat = np.zeros_like(mu) # empirical mean\n",
    "    T_ij = np.ones_like(mu) # total number of times arm (i,j) is played\n",
    "    for t in range(T):\n",
    "        y = np.random.uniform(80, 120, m).astype(int)\n",
    "        x_opt, f_opt, status = oracle(y, mu, gamma)\n",
    "        f(x_opt, y, mu, gamma)\n",
    "        if 'optimal' not in status:\n",
    "            print('Solution infeasible')\n",
    "            break\n",
    "\n",
    "        rho_ij = np.sqrt(3*np.log(t+1)/(2*T_ij))\n",
    "        mu_bar = np.max(mu_hat - rho_ij, 0) # LCB\n",
    "\n",
    "        x_t, f_t, status = oracle(y, mu_bar, gamma)\n",
    "        f_t = f(x_t, y, mu, gamma)\n",
    "\n",
    "        # sample j based on x_t[i], observe c_ij, update mu_hat[i,j]\n",
    "        c = trace_gen.generate()\n",
    "        for i in range(m):\n",
    "            j = np.random.choice(n+1, p=x_t[:,i])\n",
    "            if j != 0:\n",
    "                j -= 1\n",
    "                T_ij[i, j] += 1\n",
    "                mu_hat[i, j] += (c[i,j] - mu_hat[i, j]) / T_ij[i, j]\n",
    "\n",
    "        # calculate regert\n",
    "        reg[u, t] = f_t - f_opt\n",
    "\n",
    "plt.plot(np.cumsum(reg,axis=1).T)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyCharm (Offloading-FL)",
   "language": "python",
   "name": "pycharm-e0a56cf4"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
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